This invention relates to brain computer interface (BCI) controllers. More particularly, this invention relates to an affective BCI controller for decoding a patient's emotional experience and a regular BCI that acquires signals from the patient's brain regions to determine the patient's intention to experience a specific emotion.
Mental illnesses, for example, post traumatic stress disorder (PTSD), depression, and addiction, impair war fighters and civilians, and are a leading cause of disability and lost productivity. These illnesses can be conceptualized as brain disorders of malfunctioning neural circuits. Often, psychiatric treatments fail to cure a substantial fraction of patients, who are then declared resistant to approved therapeutic interventions. At the core of the problem is the focus on historical diagnostic categories. The National Institute of Mental Health's (NIMH) Research Domain Criteria (RDoC) project aims to develop neuroscience-based classification schemes for diagnosis and treatment of neural circuitry dysfunction. Diagnostic and Statistical Manual (DSM) diagnoses are not neurobiologic entities, but are a historical checklist-based approach of clustering symptoms used to define hypothetical constructs or syndromes. Those syndromes may not align with underlying neurobiological dysfunction in neural circuitry and corresponding behavioral (functional) domains.
Thus, attempts have been made using responsive brain stimulation systems to treat mental and emotional disorders previously treated by psychiatrists. Responsive brain stimulation is stimulation applied to the brain that responds directly to a patient's electrical brain activity or clinical features. One realization of a responsive brain stimulation system is implantable, with electrodes placed inside a patient's brain. There are a number of sites in the brain where stimulation may be applied in attempts to change a patient's emotional experiences. However, these responsive brain stimulation systems often have no proven biomarker. A biomarker may be a measurable indicator or signal from the brain or body representative of the symptoms of the illness being treated that indicates whether the symptoms have gotten better or worse. Without something reliable to sense, it is difficult for the responsive stimulator to respond accurately.
Other attempts to treat mental and emotional disorders have moved away from trying to find biomarkers for specific mental disorders, and instead have tried to find biomarkers for emotions utilizing an affective brain-computer interface (aBCI). An aBCI in combination with a brain scanner or electroencephalography (EEG) system, for example, can look at signals in real-time and determine whether the subject is having a positive-valence (e.g., happy, pleasant, etc.) or a negative-valence (e.g., angry, afraid, unpleasant, etc.) emotion. In more advanced systems, the specific emotion (e.g., anger, fear, disgust, pleasure, etc.) can be classified.
However, an aBCI alone may not be a useful clinical tool, as it cannot determine whether the emotion is a healthy emotion (e.g., anger that was justifiably provoked, fear because the patient is in a dangerous situation, etc.) from an unhealthy emotion (e.g., violent anger in response to a mild insult, fear of an ordinarily safe situation such as driving on a freeway, etc.). Therefore, there it is difficult for a controller to decide whether the emotion should be corrected or altered by stimulating the brain.
Thus, there is a clinical need for responsive neurostimulators, which sense a patient's brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful.
The need for affective BCI monitoring and decoding is clearest in deep brain stimulation (DBS). Psychiatric DBS has been used at multiple targets, with preliminary success in treating depression and obsessive-compulsive disorder (OCD), for example. Progress in psychiatric DBS, however, has been limited by its inherent open-loop nature. Present open-loop DBS systems deliver energy continuously at a pre-programmed frequency and amplitude, with parameter adjustments only during infrequent clinician visits. This has led to more rapid depletion of device batteries which requires battery replacement surgeries and introduces the patient to associated pain and/or infection. The continuous delivery of energy also leads to an increased side-effect burden. Side effects in particular derive from present devices' inability to match stimulation to a patient's current affective state, brain activity, and therapeutic need. Atop this, many disorders have symptoms that rapidly flare and remit, on a timescale of minutes to hours. This is particularly common in the anxiety and trauma related clusters. Existing open-loop DBS strategies have been unable to effectively treat such fluctuations, because the fluctuations occur on shorter timescales than the infrequent clinical visits.
However, development of closed-loop emotional DBS systems has been blocked by a lack of accurate or feasible biomarkers. Three major challenges arise when considering existing affective BCIs as the sensing component of closed-loop DBS control. First, many identified neural correlates of affective disorders cannot be continuously monitored in the community. Functional magnetic resonance imaging (fMRI) can provide deep insights into activity across the whole brain, and has been demonstrated for partial affective classification in real time. Similar results have been seen with near-infrared spectroscopy (NIRS), which also measures blood-oxygenation signals. The former, however, requires bulky machines and is not compatible with implanted devices, and the latter has not yet been demonstrated in an online-decoding paradigm. Moreover, although NIRS can be reduced to a wearable/portable device, it requires an externally worn headset. Given the unfortunate persistence of stigma attached to patients with mental disorders, few would wear a visible display of their illness, even if it did control symptoms.
Another challenge with existing affective BCIs is that affective decoding modalities that support continuous recording may not function properly in the presence of psychiatric illness. Electrocorticography (ECOG) is a promising approach, as it can be implanted, and thus hidden, with relatively minimally invasive surgery. ECOG signals offer temporal resolution and may be able to use decoders originally developed for electroencephalography (EEG). Non-invasive EEG has been a successful approach in affective BCI, with some real-time decoding of emotional information. Uncertainty arises because all successful EEG affective decoding has been demonstrated in healthy patients. Patients with mental illness, particularly those with treatment-resistant disorders, by definition do not have normal or healthy neurologic function. Furthermore, recent experiences with EEG in psychiatry suggest that measures that accurately decode healthy controls may not transfer to patients. EEG biomarkers that initially appeared to correlate with psychiatric symptoms and treatment response have often not held up under replication studies. This is at least in part because psychiatric diagnosis focuses on syndromes and symptom clusters, not etiologies. There is a wide consensus that clinical diagnoses generally contain multiple neurologic entities, and that the same clinical phenotype might arise from diametrically opposite changes in the brain. This may present a challenge for clinical translation of existing affective decoders.
Yet another challenge with existing affective BCIs is that even if affective BCIs can function in the presence of clinical symptoms, they may not be able to adequately distinguish pathologic states. Newer affective BCI algorithms may yet be shown to accurately classify emotion even in the presence of abnormal neural circuit activity, but this is only part of the need. Psychiatric disorders are marked by extremes of the same emotions that occur in everyday normal life. The difference is not the degree or type of affect, but its appropriateness to the context. PTSD is one clear example where patients with this disorder over generalize from a fearful event and experience high arousal and vigilance in contexts that are objectively safe. It is likely possible for an affective BCI to detect high arousal in a patient with PTSD in uncontrolled real-world environments. It is less clear whether any algorithm could distinguish pathologic arousal (e.g., a ‘flashback’ in a grocery store, confrontation with trauma cues, etc.) from healthy variance (e.g., riding a roller coaster, watching an exciting movie, etc.). These emotions would be very difficult to differentiate solely on the basis of experienced affect, and yet the use of brain stimulation to neutralize the latter set of experiences would negatively impact the patient's quality of life.
The above described challenges combine to reveal a final complication. In a fully implanted system, onboard storage and computational resources are limited, and therefore it may not be possible to perform decoding and tracking over long periods of time. Thus, affective decoders are caught in a dilemma of temporal resolution. If the affective decoders are tuned to respond to brief but intense events, the decoders may over-react to natural and healthy emotional variation. If the decoders instead focus only on detecting and compensating for long-term trends, sharp but short exacerbations will go uncorrected, decreasing patients' quality of life and continuing the problems of existing open-loop DBS. In the very long run, these problems may be ameliorated by improvements in battery and processing technology. However, regulatory agencies require extensive review of all new technology components, meaning that a new battery could take a decade to reach clinical use even after being successfully demonstrated for non-implantable applications. Processors might be more easily upgraded, but increased processing power means increased heat, which cannot be readily dissipated inside the body.
Thus, there is a need for systems and methods for responsive decoding and stimulation capable of operating within the limits of current clinical technology. An affective BCI usable as the sensing component of a responsive brain stimulator and capable of inferring emotional state from neural signals to enable a responsive, closed-loop stimulator is desirable. It is also desirable for continuous monitoring capable of indicating that the system is moving into a pathological state so that the controller can adjust parameters of an implanted DBS to counteract that trajectory, as well as reduce the side effects of over-stimulation, alleviate residual symptoms that may relate to under-stimulation, and improve power consumption for a longer battery life.